=Paper=
{{Paper
|id=Vol-2943/exist_paper7
|storemode=property
|title=Knowledge-based Neural Framework for Sexism Detection and Classification
|pdfUrl=https://ceur-ws.org/Vol-2943/exist_paper7.pdf
|volume=Vol-2943
|authors=Harika Abburi,Shradha Sehgal,Himanshu Maheshwari,Vasudeva Varma
|dblpUrl=https://dblp.org/rec/conf/sepln/AbburiSMV21
}}
==Knowledge-based Neural Framework for Sexism Detection and Classification==
https://ceur-ws.org/Vol-2943/exist_paper7.pdf
Knowledge-based Neural Framework for Sexism
Detection and Classification
Harika Abburi, Shradha Sehgal, Himanshu Maheshwari, and
Vasudeva Varma
LTRC, IIIT-Hyderabad, India
harika.a@research.iiit.ac.in, shradha.sehgal@students.iiit.ac.in
himanshu.maheshwari@research.iiit.ac.in, vv@iiit.ac.in
Abstract. Sexism, a prejudice that causes enormous suffering, mani-
fests in blatant as well as subtle ways. As sexist content towards women
is increasingly spread on social networks, the automatic detection and
categorization of these tweets/posts can help social scientists and policy-
makers in research, thereby combating sexism. In this paper, we explore
the problem of detecting whether a Twitter/Gab post is sexist or not. We
further discriminate the detected sexist post into one of the fine-grained
sexism categories. We propose a neural model for this sexism detec-
tion and classification that can combine representations obtained using
RoBERTa model and linguistic features such as Empath, Hurtlex, and
Perspective API by involving recurrent components. We also leverage the
unlabeled sexism data to infuse the domain-specific transformer model
into our framework. Our proposed framework also features a knowledge
module comprised of emoticon and hashtag representations to infuse the
external knowledge-specific features into the learning process. Several
proposed methods outperform various baselines across several standard
metrics.
Keywords: Sexism detection · Sexism classification · Transformers ·
Knowledge-base.
1 Introduction
Sexism encompasses stereotypes, prejudice, or discrimination based on a per-
son’s sex or gender, most often women. It occurs in various subtle and overt
forms, causing immense suffering to women and girls. Inequality and sexism
against women prevalent in society are constantly being mirrored on the inter-
net. Women are affected in many aspects of their lives, including domestic and
parental responsibilities, work prospects, sexual appearance, life desires, by the
most nuanced manifestations of sexism. So, the automatic detection and cate-
gorization of sexism into well-defined categories may help to analyze sexism to
IberLEF 2021, September 2021, Málaga, Spain.
Copyright © 2021 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
improve sensitization programs and put in place other mechanisms to combat
this oppression. It could also aid in the development, design, propagation of new
equality policies and as well as to promote positive societal action.
The detection of sexism varies from and may supplement the classification of
sexism. Sexism detection will be used to identify the sexist posts where instances
of sexism are mixed with other posts unrelated to sexism on which to perform
sexism classification. Some hate speech classification works [4, 25] detect sexism
as a type of hate; however, it does not conduct sexism classification. The prior
work on classifying sexism [3, 13, 11, 25] has identified explicit hatred or violence
towards women and classified the given tweet into two to five categories. In this
paper, we examine the problem of detecting and classifying sexism in a broad
context, from overt misogyny to other indirect phrases that include latent sexist
behaviours. More specifically, we attempt to solve two classification problems
using tweets and gab posts in Spanish and English. Firstly, we assign a binary
label to a tweet indicating whether it is sexist or not. Secondly, if the tweet is
classified as sexist, we classify it further into fine-grained sexism categories.
We develop a novel neural framework for sexism detection and classifica-
tion that enables a flexible combination of text representations generated by
the domain-specific transformer model with the linguistic and semantic fea-
ture representations through recurrent operations. Our model can be better
equipped to capture the semantic aspects by using general-purpose transformer
models like RoBERTa [17] since it is trained on text data that is much larger
than the domain-specific labeled data we have. However, in order to incorpo-
rate domain-specific elements into our model, we further retrain the RoBERTa
model using the unlabeled instances of sexism. The representations from this
domain-specific model complement the representations built from linguistic and
semantic features such as Empath [10], Hurtlex [5] and Google’s Perspective API
https://www.perspectiveapi.com/ as a function of end-to-end trainable neural
network parameters. Further, to fully comprehend the style of text, we infuse
external knowledge information into our framework by leveraging the pragmatics
of emojis, smileys, and the specific context of the hashtags as additional con-
text representations. Our experimentation has shown that multiple instances of
the proposed framework outperforming several diverse baselines on established
metrics.
Our key contributions are summarized below.
– We propose a neural framework that can combine post representations built
from different linguistic features with those created using the transformer
model through learnable model parameters.
– Our proposed framework is also aided by external knowledge information by
leveraging the hashtags and emojis present in the Tweets or Gab posts.
– The proposed methods outperform numerous baselines across established
metrics.
2 Related Work
In this section, we first look at work on hate speech detection and classification
since some of it is related to our work in some ways, such as detecting sexist
hate. We then explain previous research on sexism classification.
The detection of sexism is performed by some hate speech classification ap-
proaches that include sexism as a category of hate [25, 4, 7, 29]. [11] presents an
approach for detecting sexism and misogyny from tweets. [6] build a data-driven
model of cyberhate to identify disability, race, and sexual orientation using bag-
of-words, dictionary, and text parser to extract typed dependencies. [25, 24]
classified tweets as sexist, racist, or neither using character n-grams along with
extra-linguistic features. Deep learning algorithms such as fastText, RNN, and
CNN are investigated by [4] to classify the given tweet as racist, sexist, and nei-
ther. [30] proposed deep learning ensemble techniques on the existing datasets.
[20] provide a hierarchical Conditional Variational Autoencoder model for fine-
grained hate speech classification. [29] explored the word embeddings with a
combination of GRU and CNN and skipped CNN to classify tweets as sexism,
racism, both, and non-hate.
In [13], tweets are classified as benevolent, hostile, or non-sexist using biL-
STM with attention, SVM, and fastText. Using features such as Part of Speech
(POS) identifiers, n-grams, and text embedding, tweets described as misogynist,
are categorized as stereotype and objectification, discredit, threats of violence,
sexual harassment, and dominance, or derailing in [3]. [15] has investigated deep
learning strategies for classifying tweets of sexual violence but has not directly
focused on developing a comprehensive method to detect recollections of per-
sonal stories of abuse. [12] use ConceptNet and Wikidata to classify the tweets
related to sexual harassment by text augmentation and text generation. The
first dataset of sexist phrases and attitudes in Spanish on Twitter (MeTwo) is
created [21] and investigates the possibility of using both conventional and novel
deep learning models for automatically detecting various forms of sexist conduct.
In [27], a density matrix encoder inspired by quantum mechanics is used for
the classification of personal stories of sexual harassment. [14] explores CNN,
RNN, and a combination of them for categorizing personal experiences of sexual
harassment into one or more of three classes. [18, 1] explores multi-label catego-
rization of accounts reporting any kind(s) of sexism. They developed supervised
and semi-supervised methods for classifying sexism at the fine-grained level using
transformer models such as BERT [8]. While their study focuses on what an in-
cident of sexism entails, where it happens, and who perpetrates it, but our work
focuses on detection and categorization of sexism pertains to how it is stated.
[2] propose a multi-task approach to perform multi-label sexism classification.
They use sexism detection as one of the auxiliary tasks in a multi-task setup. In
contrast, our work performs the sexism detection task explicitly. Our work also
makes use of cutting-edge transformer models [23] that have been trained on
vast amounts of data to produce stable and semantically rich embeddings which
can be used for downstream tasks such as sexism detection and classification.
3 Dataset
In this section, we will explain both labeled and unlabeled datasets that are
used for the sexism detection and classification tasks. We also provided the label
distribution across the languages for both the tasks.
3.1 Labeled data
The dataset for the tasks were provided by the organizers of the EXIST shared
task [22]. The dataset is composed of tweets and gab posts in two languages:
English and Spanish. It consists of 6977 tweets for training and 3386 tweets
for testing. Test set also has 492 gabs in English and 490 in Spanish from the
uncensored Gab social network.
The first subtask is a binary classification, predicting whether the given text
(tweet or gab post) is sexist (i.e., it is sexist itself, describes a sexist situation or
criticizes a sexist behaviour) or non-sexist. Table 1 shows the number of posts
present in each class for both languages. Once a message has been classified
Table 1. Label distribution across the languages for sexism detection task.
Category English Spanish
Sexist 2794 2864
Non-sexist 2850 2837
as sexist, the second task aims to categorize the given text into five sexism
categories. The number of posts in each class for both languages is shown in
table 2.
1. Ideological and Inequality: This class includes text that criticises the
feminist movement, condemns gender discrimination, or portrays men as
victims of gender-based oppression.
Table 2. Label distribution across the languages for sexism classification task.
Category English Spanish
Ideological and Inequality 719 768
Stereotyping and Dominance 628 645
Objectification 406 418
Sexual Violence 542 375
Misogyny and Non-sexual Violence 499 658
2. Stereotyping and Dominance: The text presents false views about women,
such as that they are more desirable for certain positions or that they are in-
adequate for certain activities, or that men are somehow superior to women.
3. Objectification: The text portrays women as subjects separate from their
integrity and personal aspects, or defines those physical characteristics that
women must have in order to meet conventional gender norms.
4. Sexual Violence: Sexual suggestions, requests for sexual favors or harass-
ment of a sexual nature (rape or sexual assault) are made.
5. Misogyny and Non-Sexual Violence: The text expresses hatred and
violence towards women.
In this work, we converted all Spanish posts to English using the Microsoft
Translator API and use the entire data to perform the experiments.
3.2 Unlabeled data
We also investigate unlabeled data in order to construct the domain-specific
transformer model. We make use of both unlabelled (crawled) and labeled data
created by [18]. They crawled over 90,000 unlabeled examples of sexism from
the Everyday Sexism Project, which includes hundreds of thousands of sexism
accounts from witnesses and survivors. Along with these unlabeled instances, we
also use the 13023 labelled data after removing the labels. We used a total of
1,03,023 unlabeled posts in our work.
4 Proposed Sexism Detection and Classification
Approach
In this section, we detail our approaches for carrying out the detection and classi-
fication of sexist posts. We begin the section with the description of the features
explored. Next, we discuss the proposed neural architecture, which enables com-
bining different post representations by infusing external knowledge information
into the learning process.
4.1 Features Explored
We investigated linguistic and semantic features to see if they could aid in de-
tecting and classifying sexist posts.
Perspective API (Pe): Google’s Perspective API is an API that uses ma-
chine learning algorithms to predict the perceived effect of a text by analysing
different emotional concepts. The API provides scores in real numbers between
0 and 1. The API provides the following attributes for English text: toxicity, se-
vere toxicity, identity attack, insult, profanity, threat, sexual explicit, obscene,
and flirtation. For each sentence in a post, we created 9-dimensional feature
vector.
HurtLex (Hu): HurtLex is a lexicon of aggressive, offensive, and hateful
words/phrases. These hateful words are divided into 17 categories. It also has
a 2-level structure named conservative and inclusive. Conservative lemmas are
obtained by translating offensive senses of the words in the original lexicon. In-
clusive lemmas are obtained by translating all the potentially relevant senses of
the words in the original lexicon.
We consider nine categories that are relevant to our problem; negative stereo-
types ethnic slurs (PS), professions and occupations (PA), physical disabilities
and diversity (DDF), cognitive disabilities and diversity (DDP), female geni-
talia (ASF), words related to prostitution (PR), words related to homosexual-
ity (OM), with potential negative connotations (QAS), and derogatory words
(CDS). We consider both conservative and inclusive levels results in an 18-
dimensional feature vector. For each sentence in a post, we check if the lexicon’s
phrases/words present in these categories and create a feature vector with a
frequency of each category.
Empath (Em): Empath is a tool for analyzing text across lexical categories
(similar to LIWC) and can also generate new lexical categories. Empath draws
connotations between words and phrases by deep learning across more than 1.8
billion words of modern fiction.
We filtered out the built-in human-validated categories relevant to our task.
The categories chosen were sexism, violence, money, valuable, domestic work,
hate, aggression, anticipation, crime, weakness, horror, swearing terms, kill, sex-
ual, cooking, exasperation, body, ridicule, disgust, anger, and rage result in a 21
dimension feature vector.
4.2 Proposed Architecture
Output
probabilities
Knowledge Module
Fully connected
layer Roberta-t
embedding Segment Extract
Fully connected generation hashtags Hashtags
layer
R'
H' emoji2vec Extract
generation Emojis
E'
T'
Concatenate tweet representations
Re-training Module
biLSTM + biLSTM + biLSTM + biLSTM +
Attention Attention Attention Attention Pre-trained
RoBERTa
Domain
Specific re-
Perspective Hurtlex feature Empath feature Embedding RoBERTa-t Unlabeled
training
feature generation generation generation generation accounts of
sexism
Raw input text
Fig. 1. Proposed knowledge-based neural architecture
Figure 1 depicts our proposed architecture. Each tweet or gab post (raw
input text) is passed to the Perspective API, Empath tool, Hurtlex lexicon,
and transformer model to generate the sentence representations. We employ a
domain-adapted RoBERTa variant named RoBERTa t for generating more ef-
fective sentence representations than those produced by off-the-shelf RoBERTa
models. RoBERTa t is created by further training a pre-trained RoBERTa model
in an unsupervised manner using unlabelled accounts of sexism. We incorporate
RoBERTa t into our end-to-end training and the weights are updated during
the training. Further, these sentence representations are passed to the bidirec-
tional LSTM and an associated attention mechanism [28] to generate the entire
text representation. For each sentence in a text, the biLSTM layer produces an
h-dimensional output length. These output lengths are aggregated into a vector
representation by the attention layer. Overall, this results in four different text
representations, which are then concatenated to generate the final post repre-
sentation T 0 .
To improve the efficiency of our model, we add external information to
the current architecture, which includes knowledge of the pragmatics of emo-
jis and smileys, as well as the context in which those hashtags are used. The
motivation behind this is that the model may not capture the characteris-
tics and true meaning of emojis and hashtags present in the text. We utilize
emoji2vec [9] to obtain a semantic vector representing the particular emoji.
The hashtags are segment into meaningful tokens using the ekphrasis segmenter
https://github.com/cbaziotis/ekphrasis. We generate the segmented hashtag em-
beddings using the RoBERTa t such that the text representations and hashtag
embeddings are grounded in the same latent space. For each input text, to ob-
tain the centralized emoji representation E 0 and hashtag representation H 0 , we
average the vector representations of all the individual emojis and the segmented
hashtags. Then we concatenate all three representations T 0 , E 0 , and H 0 to get
the final tweet representation R0 . This R0 is passed to the fully connected layer.
Finally, a fully connected layer with nonlinearity generates the output probabil-
ities.
5 Experiments
This section provides the experimental evaluation of the proposed methods
present analysis. Our code, as well as all hyper-parameter values, are available
at GitHub https://github.com/Harikavuppala1a/EXIST_shared-task.
For sexism detection we reported results on F score (F ) and Accuracy (Acc)
and for sexism classification results are reported on F weighted (Fw ), F macro
(Fmacro ), and Accuracy (Acc).
5.1 Baselines
Random
For each test sample, labels are selected randomly as per their normalized
frequencies in the training data.
Traditional Machine Learning (TML)
We report the performance using Support Vector Machine (SVM), Logistic
Regression (LR), Gradient Boosted Trees (GBT), and Random Forests (RF),
each applied on three feature sets, namely the word n-grams, character n-grams,
and the average of the ELMo vectors [19] for a post’s words (ELMO).
Deep Learning (DL)
– biLSTM: The word embeddings correspond to each post are fed through a
bidirectional LSTM.
– biLSTM-Attention: The biLSTM-Attention is similar to biLSTM, but with
the attention scheme from [28].
– C-biLSTM: This architecture is somewhat similar to approach [14]. After
the convolution operation has been carried out on the word vectors of each
post, the filter dimensions are stacked to create a series of window vectors
that are then transmitted through biLSTM.
– CNN-Kim: Word vectors of a post are passed through convolutional and
max-over-time pooling layers similar to [16].
– BERT and RoBERTa : Sentence embeddings are generated using BERT
via bert-as-service [26] and RoBERTa and passed through a biLSTM with
attention separately.
Table 3. Results with traditional machine learning: (1) Sexism detection (2) Sexism
classification
Sexism Detection Sexism Classification
Classifier Features
F Acc FW Fmacro Acc
Word n-grams 0.691 0.689 0.568 0.481 0.572
SVM Character n-grams 0.697 0.698 0.574 0.485 0.578
ELMo average 0.662 0.651 0.474 0.372 0.472
Word n-grams 0.703 0.707 0.507 0.380 0.574
LR Character n-grams 0.686 0.701 0.511 0.377 0.580
ELMo average 0.672 0.669 0.520 0.413 0.537
Word n-grams 0.676 0.695 0.560 0.454 0.602
GBT Character n-grams 0.687 0.703 0.540 0.422 0.594
ELMo average 0.686 0.689 0.482 0.343 0.548
Word n-grams 0.717 0.712 0.557 0.457 0.591
RF Character n-grams 0.680 0.696 0.520 0.397 0.582
ELMo average 0.644 0.667 0.356 0.164 0.499
5.2 Results
We set aside 15% from original labeled data for validation. During the testing
phase, the validation set was merged with the training set. For all the methods,
the mean of the results obtained over three runs is given for each metric. Most
proposed methods outperform all baselines across all metrics.
Table 3 shows results produced using four traditional ML methods (SVM,
LR, GBT, and RF) across three different feature sets (word n-grams, character
Table 4. Results for Sexism detection (baselines use ELMo embeddings)
Approach F Acc
Random 0.502 0.498
Baselines
biLSTM 0.691 0.698
biLSTM-Attention 0.731 0.716
CNN-Kim 0.740 0.713
C-biLSTM 0.738 0.720
BERT 0.705 0.697
RoBERTa 0.725 0.712
biL-att applied on External knowledge
RoBERTa t 0.752 0.749
RoBERTa t Hashtag 0.761 0755
RoBERTa t Emoji, Hashtag 0.756 0.754
Proposed methods
RoBERTa t, Em Emoji 0.765 0.759
RoBERTa t, Em Hashtag 0.761 0.758
RoBERTa t, Hu Emoji 0.768 0.759
RoBERTa t, Pe 0.765 0.756
RoBERTa t, Pe, Hu 0.760 0.757
RoBERTa t, Pe, Hu Emoji, Hashtag 0.763 0.754
RoBERTa t, Em, Pe 0.765 0.756
RoBERTa t, Em, Pe Emoji, Hashtag 0.761 0.756
RoBERTa t, Pe, Em, Hu 0.763 0.758
RoBERTa t, Pe, Em, Hu Emoji 0.764 0.757
RoBERTa t, Hu, Em Hashtag 0.759 0.755
RoBERTa t, Hu, Em 0.766 0.760
n-grams, and ELMo average) for both the tasks. For SVM and LR, we apply
class imbalance correction across both tasks. Among these combinations, RF
with word n-grams emerges as the top sexism detection method. SVM with
character n-grams produces the best F scores for sexism classification.
Table 4 and Table 5 provides sexism detection and sexism classification re-
sults for random, deep learning and various combinations of proposed framework.
For proposed methods, the sub-columns in each row specify which neural method
or linguistic features are used to generate post representations and the knowl-
edge information employed. We note that the results are reported for a subset
of the possible outcomes of the proposed neural framework.
In Table 4, for sexism detection task, the random method performs poorly
as expected. The best deep learning baseline is C-biLSTM based on Acc, and
it outperforms its traditional ML counterpart. Our best method involves bil-att
processing on RoBERTa t, Hu, and Em separately to generate the final post
representations. When external information such as Emoji and Hashtag repre-
sentations are concatenated with this best method, the performance is marginally
reduced. In Table 5, also the random method performs poorly as expected, re-
flecting the challenging nature of the sexism classification. The best deep learning
baseline is biLSTM-Attention based on Fmacro , and it outperforms its traditional
ML counterpart. Among the various combinations of proposed methods, our best
Table 5. Results for Sexism classification (baselines use ELMo embeddings)
Approach Fw Fmacro Acc
Baselines Random 0.292 0.165 0.299
biLSTM 0.561 0.481 0.558
biLSTM-Attention 0.571 0.505 0.564
CNN-Kim 0.564 0.470 0.588
C-biLSTM 0.582 0.500 0.587
BERT 0.513 0.449 0.499
RoBERTa 0.536 0.462 0.527
biL-att applied on External knowledge
RoBERTa t 0.626 0.544 0.629
RoBERTa t Emoji 0.632 0.551 0.636
RoBERTa t Hashtag 0.630 0.548 0.634
Proposed methods
RoBERTa t Emoji, Hashtag 0.632 0.549 0.635
RoBERTa t, Em Emoji 0.633 0.552 0.637
RoBERTa t, Em Emoji, Hashtag 0.633 0.550 0.635
RoBERTa t, Pe Hashtag 0.633 0.551 0.639
RoBERTa t, Hu Emoji 0.631 0.549 0.637
RoBERTa t, Em, Pe Hashtag 0.633 0.554 0.634
RoBERTa t, Em, Pe Emoji, Hashtag 0.632 0.550 0.635
RoBERTa t, Hu, Em Emoji, Hashtag 0.628 0.547 0.630
RoBERTa t, Pe, Em, Hu Hashtag 0.630 0.548 0.633
RoBERTa t, Pe, Em, Hu Emoji, Hashtag 0.628 0.545 0.629
RoBERTa t, Pe, Hu 0.629 0.545 0.634
RoBERTa t, Pe, Hu Emoji 0.633 0.551 0.637
RoBERTa t, Pe, Hu Hashtag 0.635 0.555 0.638
method uses the biL-att processing on RoBERTa t, Pe, and Em features along
with the external hashtag context vector.
Overall, we observed that linguistic features, when combined with the domain-
specific transformer model involving recurrent components, help to improve the
detection and classification performance to some extent. It has also been noted
that in most cases, the external knowledge information (Emoji and Hashtag
features) helps to improve the performance even further. Furthermore, several
variants of the proposed framework outperform all baselines across all the met-
rics.
Figure 2 compares the class-wise performance of our best method (Roberta t,
Pe, Hu, Hashtag) with that of the best baseline (biLSTM-Attention) for sexism
classification. For each class, the average of the F scores over three runs are
shown for both methods. For all the classes, the F score of the proposed method
outperforms the baseline F score.
We analyze the impact of our proposed sexism detection and classification
methods on different social networks, including Twitter (“with consent control”)
and Gab.com (“without content control”). Table 6 shows the total number of
posts present in the test set for each network and the number of correctly pre-
dicted posts by baselines and proposed methods for both tasks. Our proposed
0.9 1. Ideological-inequality
0.8 F score for the best baseline
F score for our best method 2. Stereotyping-dominance
0.7 3. Objectification
0.6 4. Sexual-violence
0.5 5. Misogyny-non-sexual-violence
0.4 6. Non-sexist
0.3
0.2
0.1
0 1 2 3 4 5 6
Label IDs
Fig. 2. Class-wise sexism classification F-scores for the best performing baseline
(biLSTM-Attention) and our best method (Roberta t, Pe, Hu, Hashtag)
approaches perform effectively on both social media posts compared to the base-
lines with a reasonable margin for both tasks.
Table 6. Analysis of different social media posts: (1) Sexism detection: Best baseline
(C-biLSTM), Best proposed method (Roberta t, Hu, Em) (2) Sexism classification: Best
baseline (biLSTM-Attention), Best proposed method (RoBERTa t, Pe, Hu, Hashtag)
Sexism Detection Sexism Classification
Type of posts Posts in test set
Best baseline Best proposed Best baseline Best proposed
method method
Tweets 3386 2437 2564 1878 2187
Gab posts 982 726 749 532 610
6 Conclusion
In this paper, we explored the sexism detection and fine-grained classification of
tweets and Gab posts. We developed a knowledge-based neural framework that
combines representations created using linguistic features and those obtained
using the RoBERTa model trained in an end-to-end manner. We capitalized on
unlabeled data to build the domain-specific transformer model. Our experiments
show that the external knowledge representations fed into the neural framework
aided in boosting the performance. All the variants of the proposed approach
outperforms several deep learning and traditional machine learning baselines.
Our analysis showed that our proposed approach is also effective at detecting
and classifying the posts in Gab.com where the abusive content is not restricted.
Directions for future work include developing approaches that conduct sexism
classification more accurately as well as exploring the multilingual sexism detec-
tion and classification.
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